风险数据经理
Exposure Data Manager
关于公司
Accelerant（NYSE: ARX）是一家数据驱动的风险交易所，通过数据、高级分析和人工智能驱动的洞察力，重塑专业保险。我们的市场和专有技术平台使中小型企业能够通过连接专业风险承保人（通常为MGA、自保经理、零售经纪人）与风险资本提供方（保险公司、再保险公司、机构投资者）而蓬勃发展，打造更高效、透明的专业保险生态系统，拥有更智能的产品、数据和人工智能。公司成立于2018年底，截至2025年第三季度，过去12个月在Accelerant风险交易所交易的年保费超过40亿美元。如需更多信息，请访问 。
职位描述：
这不是软件工程、机器学习工程或数据基础设施岗位。我们寻找具备保险领域专业知识、使用分析来支持承保和风险决策的候选人。没有保险行业经验的候选人将不被考虑。
作为风险敞口数据经理，您将：
- 领导分析和调查工作，帮助业务理解风险敞口模式和集中风险
- 定义并执行数据标准、质量控制和最佳实践，适用于各业务线的风险敞口数据
- 负责数据质量和完整性——您不仅收集反馈，还能从行业经验中直觉判断需要修复的问题，并与其他部门合作制定永久性解决方案，以可靠地生成业内领先的风险敞口数据
主要职责
- 主导业务对来自内部和外部来源的风险敞口数据的摄入、转换和标准化的监督，利用承保、精算或相关知识
- 验证和质量保证风险敞口数据：识别异常、缺口、重复、不一致之处，并推动改进
- 与精算、承保、灾害建模和产品团队合作，了解他们的风险敞口需求
- 开发和维护风险敞口数据文档、数据字典和流程指南
- 支持分析用例（例如风险集中、投资组合压力测试、情景分析）
- 建立、监控和跟踪数据质量KPI，构建仪表盘或警报以主动发现问题
- 支持临时分析，诊断风险敞口趋势、集中风险、费率分析以及所需的承保行动
- 为将风险敞口数据集成到下游工具（例如建模引擎、定价系统、商业智能工具）提供指导
资格要求
· 精通风险敞口数据管理，具有保险行业背景，熟悉承保、精算或风险管理流程
· 具备强大的数据分析能力，能够处理复杂数据集并从中提取洞见
· 优秀的沟通和协作能力，能够与跨职能团队有效合作
· 熟悉数据质量管理工具和方法论
· 对数据驱动的决策有热情，能够推动持续改进
查看英文原文
About Us
Accelerant(NYSE: ARX)isais a data-driven risk exchangetransforming specialty insurance through data,advanced analytics,andAI driven insights.Our marketplace and proprietary technology platformempowerssmall-to-medium businesses to thrive by connecting specialty risk underwriters (typically MGAs, captive managers, retail brokers) with risk capital providers (Insurers, Reinsurers, Institutional Investors), creating more efficient and transparent ecosystem for specialty insurance with smarter products, data and AI.Startedin late 2018, over $4B of annual premiumwas transacted on the Accelerant Risk Exchangein the last 12 months ended Q3 2025.For more information, please visit .
About the Role:
This is not a software engineering, machine learning engineering, or data infrastructure role. We are looking for candidates with insurance domain expertise who use analytics to inform underwriting and exposure decisions. Candidates with no prior insurance experience will not be considered.
As the Exposure Data Manager, you will:
- Lead analytics and investigations that help the business understand exposure patterns and accumulation risk
- Define and enforce data standards, quality controls, and best practices for exposure data across business lines
- Own data quality and completeness - you not only collect feedback, but intuit what needs to be fixed from your industry experience, and collaborate with other departments on permanent solutions to reliably produce best in class exposure data
Key Responsibilities
- Lead the business oversight into ingestion, transformation, and normalization of exposure data from internal and external sources, using underwriting, Actuarial or adjacent knowledge
- Validate and QA exposure data: identify anomalies, gaps, duplicates, inconsistencies, and drive improvements
- Partner with actuarial, underwriting, catastrophe modeling, and product teams to understand their exposure needs
- Develop and maintain exposure data documentation, data dictionaries, and process guidelines
- Enable and support analytical use cases (e.g. accumulation risk, portfolio stress testing, scenario analysis)
- Build, monitor and track data quality KPIs, build dashboards or alerts to surface issues proactively
- Support ad hoc analysis to diagnose exposure trends, concentration risk, rate analysis, and required underwriting actions
- Provide guidance on integrating exposure data into downstream tools (e.g. modeling engines, pricing systems, BI)
Qualifications / Skills
Required:
- Must have experience in commercial insurance underwriting, actuarial, catastrophe modeling, or exposure management
- Bachelor’s degree in a quantitative discipline (mathematics, statistics, engineering, computer science, actuarial science)
- 3–4+ years of experience with exposure / insurance loss / policy data or related domain
- Strong proficiency in SQL; complex query and data transformation skills
- Familiarity with exposure modeling or catastrophe modeling workflows
- Experience with cloud data warehouses like Snowflake
- Experience working with insurance carrier data pipelines
- Experience in data cleansing, validation, and QA
- Analytical mindset with strong problem-solving skills
- Excellent communication skills with both technical and non-technical stakeholders
- Self-starter with strong ownership and initiative
Preferred:
- Familiarity with MGA and delegated authority exposure data
- Python or R experience for data manipulation and validation
- Version control and orchestration tools (Git, dbt, Airflow)
- Data governance or metadata management experience
Originally posted on Himalayas